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      <h1 class="site-logo" id="site-title">深入浅出PyTorch</h1>
      
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  <a class="reference internal" href="../%E7%AC%AC%E4%B8%80%E7%AB%A0/index.html">
   第一章：PyTorch的简介和安装
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     1.1 PyTorch简介
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     1.2 PyTorch的安装
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     1.3 PyTorch相关资源
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   第二章：PyTorch基础知识
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     2.1 张量
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     2.2 自动求导
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     2.3 并行计算简介
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   第三章：PyTorch的主要组成模块
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     3.1 思考：完成深度学习的必要部分
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     3.2 基本配置
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     3.3 数据读入
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     3.4 模型构建
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     3.5 模型初始化
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     3.6 损失函数
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     3.7 训练和评估
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     3.8 可视化
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     3.9 Pytorch优化器
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   第四章：PyTorch基础实战
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     基础实战——FashionMNIST时装分类
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   第五章：PyTorch模型定义
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     5.1 PyTorch模型定义的方式
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    <a class="reference internal" href="../%E7%AC%AC%E4%BA%94%E7%AB%A0/5.2%20%E5%88%A9%E7%94%A8%E6%A8%A1%E5%9E%8B%E5%9D%97%E5%BF%AB%E9%80%9F%E6%90%AD%E5%BB%BA%E5%A4%8D%E6%9D%82%E7%BD%91%E7%BB%9C.html">
     5.2 利用模型块快速搭建复杂网络
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    <a class="reference internal" href="../%E7%AC%AC%E4%BA%94%E7%AB%A0/5.3%20PyTorch%E4%BF%AE%E6%94%B9%E6%A8%A1%E5%9E%8B.html">
     5.3 PyTorch修改模型
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     5.4 PyTorch模型保存与读取
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   第六章：PyTorch进阶训练技巧
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     6.1 自定义损失函数
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     6.2 动态调整学习率
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     6.3 模型微调-torchvision
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     6.3 模型微调 - timm
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     6.4 半精度训练
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     6.5 数据增强-imgaug
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    <a class="reference internal" href="6.6%20%E4%BD%BF%E7%94%A8argparse%E8%BF%9B%E8%A1%8C%E8%B0%83%E5%8F%82.html">
     6.6 使用argparse进行调参
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    <a class="reference internal" href="PyTorch%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E4%B8%8E%E8%BF%9B%E9%98%B6%E8%AE%AD%E7%BB%83%E6%8A%80%E5%B7%A7.html">
     PyTorch模型定义与进阶训练技巧
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   第七章：PyTorch可视化
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     7.1 可视化网络结构
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    <a class="reference internal" href="../%E7%AC%AC%E4%B8%83%E7%AB%A0/7.2%20CNN%E5%8D%B7%E7%A7%AF%E5%B1%82%E5%8F%AF%E8%A7%86%E5%8C%96.html">
     7.2 CNN可视化
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     7.3 使用TensorBoard可视化训练过程
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   第八章：PyTorch生态简介
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     8.1 本章简介
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     8.2 torchvision
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     8.3 PyTorchVideo简介
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     8.4 torchtext简介
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     transforms实战
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 <li class="toc-h2 nav-item toc-entry">
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   timm的安装
  </a>
 </li>
 <li class="toc-h2 nav-item toc-entry">
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   如何查看预训练模型种类
  </a>
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   使用和修改预训练模型
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   模型的保存
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                <h1>6.3 模型微调 - timm</h1>
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   timm的安装
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   如何查看预训练模型种类
  </a>
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   使用和修改预训练模型
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   模型的保存
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  <section class="tex2jax_ignore mathjax_ignore" id="timm">
<h1>6.3 模型微调 - timm<a class="headerlink" href="#timm" title="永久链接至标题">#</a></h1>
<p>除了使用<code class="docutils literal notranslate"><span class="pre">torchvision.models</span></code>进行预训练以外，还有一个常见的预训练模型库，叫做<code class="docutils literal notranslate"><span class="pre">timm</span></code>，这个库是由来自加拿大温哥华Ross Wightman创建的。里面提供了许多计算机视觉的SOTA模型，可以当作是torchvision的扩充版本，并且里面的模型在准确度上也较高。在本章内容中，我们主要是针对这个库的预训练模型的使用做叙述，其他部分内容（数据扩增，优化器等）如果大家感兴趣，可以参考以下两个链接。</p>
<ul class="simple">
<li><p>Github链接：https://github.com/rwightman/pytorch-image-models</p></li>
<li><p>官网链接：https://fastai.github.io/timmdocs/
https://rwightman.github.io/pytorch-image-models/</p></li>
</ul>
<section id="id1">
<h2>timm的安装<a class="headerlink" href="#id1" title="永久链接至标题">#</a></h2>
<p>关于timm的安装，我们可以选择以下两种方式进行：</p>
<ol class="simple">
<li><p>通过pip安装</p></li>
</ol>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>pip install timm
</pre></div>
</div>
<ol class="simple">
<li><p>通过git与pip进行安装</p></li>
</ol>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/rwightman/pytorch-image-models
<span class="nb">cd</span> pytorch-image-models <span class="o">&amp;&amp;</span> pip install -e .
</pre></div>
</div>
</section>
<section id="id2">
<h2>如何查看预训练模型种类<a class="headerlink" href="#id2" title="永久链接至标题">#</a></h2>
<ol class="simple">
<li><p>查看timm提供的预训练模型
截止到2022.3.27日为止，timm提供的预训练模型已经达到了592个，我们可以通过<code class="docutils literal notranslate"><span class="pre">timm.list_models()</span></code>方法查看timm提供的预训练模型（注：本章测试代码均是在jupyter notebook上进行）</p></li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">timm</span>
<span class="n">avail_pretrained_models</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">list_models</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">len</span><span class="p">(</span><span class="n">avail_pretrained_models</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="m">592</span>
</pre></div>
</div>
<ol class="simple">
<li><p>查看特定模型的所有种类
每一种系列可能对应着不同方案的模型，比如Resnet系列就包括了ResNet18，50，101等模型，我们可以在<code class="docutils literal notranslate"><span class="pre">timm.list_models()</span></code>传入想查询的模型名称（模糊查询），比如我们想查询densenet系列的所有模型。</p></li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">all_densnet_models</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">list_models</span><span class="p">(</span><span class="s2">&quot;*densenet*&quot;</span><span class="p">)</span>
<span class="n">all_densnet_models</span>
</pre></div>
</div>
<p>我们发现以列表的形式返回了所有densenet系列的所有模型。</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="o">[</span><span class="s1">&#39;densenet121&#39;</span>,
 <span class="s1">&#39;densenet121d&#39;</span>,
 <span class="s1">&#39;densenet161&#39;</span>,
 <span class="s1">&#39;densenet169&#39;</span>,
 <span class="s1">&#39;densenet201&#39;</span>,
 <span class="s1">&#39;densenet264&#39;</span>,
 <span class="s1">&#39;densenet264d_iabn&#39;</span>,
 <span class="s1">&#39;densenetblur121d&#39;</span>,
 <span class="s1">&#39;tv_densenet121&#39;</span><span class="o">]</span>
</pre></div>
</div>
<ol class="simple">
<li><p>查看模型的具体参数
当我们想查看下模型的具体参数的时候，我们可以通过访问模型的<code class="docutils literal notranslate"><span class="pre">default_cfg</span></code>属性来进行查看，具体操作如下</p></li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;resnet34&#39;</span><span class="p">,</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">default_cfg</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">&#39;url&#39;</span><span class="p">:</span> <span class="s1">&#39;https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth&#39;</span><span class="p">,</span>
 <span class="s1">&#39;num_classes&#39;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
 <span class="s1">&#39;input_size&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
 <span class="s1">&#39;pool_size&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span>
 <span class="s1">&#39;crop_pct&#39;</span><span class="p">:</span> <span class="mf">0.875</span><span class="p">,</span>
 <span class="s1">&#39;interpolation&#39;</span><span class="p">:</span> <span class="s1">&#39;bilinear&#39;</span><span class="p">,</span>
 <span class="s1">&#39;mean&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">),</span>
 <span class="s1">&#39;std&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">),</span>
 <span class="s1">&#39;first_conv&#39;</span><span class="p">:</span> <span class="s1">&#39;conv1&#39;</span><span class="p">,</span>
 <span class="s1">&#39;classifier&#39;</span><span class="p">:</span> <span class="s1">&#39;fc&#39;</span><span class="p">,</span>
 <span class="s1">&#39;architecture&#39;</span><span class="p">:</span> <span class="s1">&#39;resnet34&#39;</span><span class="p">}</span>
</pre></div>
</div>
<p>除此之外，我们可以通过访问这个<a class="reference external" href="https://rwightman.github.io/pytorch-image-models/results/">链接</a> 查看提供的预训练模型的准确度等信息。</p>
</section>
<section id="id3">
<h2>使用和修改预训练模型<a class="headerlink" href="#id3" title="永久链接至标题">#</a></h2>
<p>在得到我们想要使用的预训练模型后，我们可以通过<code class="docutils literal notranslate"><span class="pre">timm.create_model()</span></code>的方法来进行模型的创建，我们可以通过传入参数<code class="docutils literal notranslate"><span class="pre">pretrained=True</span></code>，来使用预训练模型。同样的，我们也可以使用跟torchvision里面的模型一样的方法查看模型的参数，类型/</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">timm</span>
<span class="kn">import</span> <span class="nn">torch</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;resnet34&#39;</span><span class="p">,</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">output</span><span class="o">.</span><span class="n">shape</span>
</pre></div>
</div>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torch.Size<span class="o">([</span><span class="m">1</span>, <span class="m">1000</span><span class="o">])</span>
</pre></div>
</div>
<ul class="simple">
<li><p>查看某一层模型参数（以第一层卷积为例）</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;resnet34&#39;</span><span class="p">,</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">list</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">())[</span><span class="s1">&#39;conv1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
 <span class="n">tensor</span><span class="p">([[[[</span><span class="o">-</span><span class="mf">2.9398e-02</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.6421e-02</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.8832e-02</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.8349e-02</span><span class="p">,</span>
            <span class="o">-</span><span class="mf">6.9210e-03</span><span class="p">,</span>  <span class="mf">1.2127e-02</span><span class="p">],</span>
           <span class="p">[</span><span class="o">-</span><span class="mf">3.6199e-02</span><span class="p">,</span> <span class="o">-</span><span class="mf">6.0810e-02</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.3891e-02</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.2744e-02</span><span class="p">,</span>
            <span class="o">-</span><span class="mf">7.3169e-03</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.1834e-02</span><span class="p">],</span>
            <span class="o">...</span>
           <span class="p">[</span> <span class="mf">8.4563e-03</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.7099e-02</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.2176e-03</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">7.0081e-02</span><span class="p">,</span>
             <span class="mf">2.9756e-02</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.1400e-03</span><span class="p">]]]],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
            
</pre></div>
</div>
<ul class="simple">
<li><p>修改模型（将1000类改为10类输出）</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;resnet34&#39;</span><span class="p">,</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">output</span><span class="o">.</span><span class="n">shape</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
</pre></div>
</div>
<ul class="simple">
<li><p>改变输入通道数（比如我们传入的图片是单通道的，但是模型需要的是三通道图片）
我们可以通过添加<code class="docutils literal notranslate"><span class="pre">in_chans=1</span></code>来改变</p></li>
</ul>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">&#39;resnet34&#39;</span><span class="p">,</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">in_chans</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="id4">
<h2>模型的保存<a class="headerlink" href="#id4" title="永久链接至标题">#</a></h2>
<p>timm库所创建的模型是<code class="docutils literal notranslate"><span class="pre">torch.model</span></code>的子类，我们可以直接使用torch库中内置的模型参数保存和加载的方法，具体操作如下方代码所示</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span><span class="s1">&#39;./checkpoint/timm_model.pth&#39;</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./checkpoint/timm_model.pth&#39;</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="id5">
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<li><p>https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055</p></li>
<li><p>https://chowdera.com/2022/03/202203170834122729.html</p></li>
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